OpenRaiser/Intern-Atlas huggingface dataset:
Optimization of the n-dimensional sliding window inter-channel correlation algorithm for multi-core architecture
Venue: arXiv cs.DS 2018
Year: 2018

Calculating the correlation in a sliding window is a common method of statistical evaluation of the interconnect between two sets of data. And although the calculation of a single correlation coefficient is not resource-intensive and algorithmically complex, sequential computation in a large number of windows on large data sets can take quite a long time. In this case, each value in the data, falling into different windows, will be processed many times, increasing the complexity of the algorithm and the processing time. We took this fact into account and optimized the correlation calculation in the sliding window, reducing the number of operations in the overlapping area of the windows. In addition, we developed a parallel version of the optimized algorithm for the GPU architecture. Experimental studies have shown that for a 7x7 correlation window sliding in one pixel increments, we were able to accelerate the processing of an 12 MPixel image pixels on the GPU by about 60 times compared to the serial version running on the CPU. The article presents an optimized version of the algorithm, a scheme for its parallelization, as well as the results of experimental studies.
Analytical Models of the Performance of C-V2X Mode 4 Vehicular Communications
Venue: arXiv cs.NI 2018
Year: 2018

The C-V2X or LTE-V standard has been designed to support V2X (Vehicle to Everything) communications. The standard is an evolution of LTE, and it has been published by the 3GPP in Release 14. This new standard introduces the C-V2X or LTE-V Mode 4 that is specifically designed for V2V communications using the PC5 sidelink interface without any cellular infrastructure support. In Mode 4, vehicles autonomously select and manage their radio resources. Mode 4 is highly relevant since V2V safety applications cannot depend on the availability of infrastructure-based cellular coverage. This paper presents the first analytical models of the communication performance of C-V2X or LTE-V Mode 4. In particular, the paper presents analytical models for the average PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver, and for the four different types of transmission errors that can be encountered in C-V2X Mode 4. The models are validated for a wide range of transmission parameters and traffic densities. To this aim, this study compares the results obtained with the analytical models to those obtained with a C-V2X Mode 4 simulator implemented over Veins.
A tight lower bound for the hardness of clutters
Venue: arXiv cs.DM 2018
Year: 2018

A {\it clutter} (or {\it antichain} or {\it Sperner family}) $L$ is a pair $(V,E)$, where $V$ is a finite set and $E$ is a family of subsets of $V$ none of which is a subset of another. Normally, the elements of $V$ are called {\it vertices} of $L$, and the elements of $E$ are called {\it edges} of $L$. A subset $s_e$ of an edge $e$ of a clutter is {\it recognizing} for $e$, if $s_e$ is not a subset of another edge. The {\it hardness} of an edge $e$ of a clutter is the ratio of the size of $e\textrm{'s}$ smallest recognizing subset to the size of $e$. The hardness of a clutter is the maximum hardness of its edges. In this short note we prove a lower bound for the hardness of an arbitrary clutter. Our bound is asymptotically best-possible in a sense that there is an infinite sequence of clutters attaining our bound.
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
Venue: arXiv cs.LG 2018
Year: 2018

This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), and a family of machine learning classifiers and regressors (Logistic Regression, SVMs, Least Square Regression, etc.). This framework makes it possible to deploy and train models with a few lines of code, and also extend and build upon this by integrating new loss functions and optimization algorithms.
Expressive power of outer product manifolds on feed-forward neural networks
Venue: arXiv cs.LG 2018
Year: 2018

Hierarchical neural networks are exponentially more efficient than their corresponding "shallow" counterpart with the same expressive power, but involve huge number of parameters and require tedious amounts of training. Our main idea is to mathematically understand and describe the hierarchical structure of feedforward neural networks by reparametrization invariant Riemannian metrics. By computing or approximating the tangent subspace, we better utilize the original network via sparse representations that enables switching to shallow networks after a very early training stage. Our experiments show that the proposed approximation of the metric improves and sometimes even surpasses the achievable performance of the original network significantly even after a few epochs of training the original feedforward network.
Beyond Heuristics: Learning Visualization Design
Venue: arXiv cs.HC 2018
Year: 2018

In this paper, we describe a research agenda for deriving design principles directly from data. We argue that it is time to go beyond manually curated and applied visualization design guidelines. We propose learning models of visualization design from data collected using graphical perception studies and build tools powered by the learned models. To achieve this vision, we need to 1) develop scalable methods for collecting training data, 2) collect different forms of training data, 3) advance interpretability of machine learning models, and 4) develop adaptive models that evolve as more data becomes available.
A Framework for Moment Invariants
Venue: arXiv cs.CV 2018
Year: 2018

For more than half a century, moments have attracted lot ot interest in the pattern recognition community.The moments of a distribution (an object) provide several of its characteristics as center of gravity, orientation, disparity, volume. Moments can be used to define invariant characteristics to some transformations that an object can undergo, commonly called moment invariants. This work provides a simple and systematic formalism to compute geometric moment invariants in n-dimensional space.
Devam vs. Tamam: 2018 Turkish Elections
Venue: arXiv cs.SI 2018
Year: 2018

On June 24, 2018, Turkey held a historical election, transforming its parliamentary system to a presidential one. One of the main questions for Turkish voters was whether to start this new political era with reelecting its long-time political leader Recep Tayyip Erdogan or not. In this paper, we analyzed 108M tweets posted in the two months leading to the election to understand the groups that supported or opposed Erdogan's reelection. We examined the most distinguishing hashtags and retweeted accounts for both groups. Our findings indicate strong polarization between both groups as they differ in terms of ideology, news sources they follow, and preferred TV entertainment.
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Venue: arXiv cs.CV 2018
Year: 2018

Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discriminator takes three pairs of query-conditioned summaries as the input to discriminate the real summary from a generated and a random one. A three-player loss is introduced for joint training of the generator and the discriminator, which forces the generator to learn better summary results, and avoids the generation of random trivial summaries. Experiments on a recently proposed query-conditioned video summarization benchmark dataset show the efficiency and efficacy of our proposed method.
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
Venue: arXiv cs.CL 2018
Year: 2018

Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we employ a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed method achieves an accuracy of 97.0\% in the severity classification of CAD.
Deterministic oblivious distribution (and tight compaction) in linear time
Venue: arXiv cs.DS 2018
Year: 2018

In an array of N elements, M positions and M elements are "marked". We show how to permute the elements in the array so that all marked elements end in marked positions, in time O(N) (in the standard word-RAM model), deterministically, and obliviously - i.e. with a sequence of memory accesses that depends only on N and not on which elements or positions are marked. As a corollary, we answer affirmatively to an open question about the existence of a deterministic oblivious algorithm with O(N) running time for tight compaction (move the M marked elements to the first M positions of the array), a building block for several cryptographic constructions. Our O(N) result improves the running-time upper bounds for deterministic tight compaction, for randomized tight compaction, and for the simpler problem of randomized loose compaction (move the M marked elements to the first O(M) positions) - until now respectively O(N lg N), O(N lg lg N), and O(N lg*N).
Addressing Security and Privacy Challenges in Internet of Things
Venue: arXiv cs.CR 2018
Year: 2018

Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a holistic and transformative approach for providing numerous services. The rapid development of various communication protocols and miniaturization of transceivers along with recent advances in sensing technologies offer the opportunity to transform isolated devices into communicating smart things. Smart things, that can sense, store, and even process electrical, thermal, optical, chemical, and other signals to extract user-/environment-related information, have enabled services only limited by human imagination. Despite picturesque promises of IoT-enabled systems, the integration of smart things into the standard Internet introduces several security challenges because the majority of Internet technologies, communication protocols, and sensors were not designed to support IoT. Several recent research studies have demonstrated that launching security/privacy attacks against IoT-enabled systems, in particular wearable medical sensor (WMS)-based systems, may lead to catastrophic situations and life-threatening conditions. Therefore, security threats and privacy concerns in the IoT domain need to be proactively studied and aggressively addressed. In this thesis, we tackle several domain-specific security/privacy challenges associated with IoT-enabled systems.
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limited segmentation performance, since large amounts of spatial information of prostate volumes are discarded during the slice-by-slice segmentation process; and 3D methods also have room for improvement, since they use isotropic kernels to perform 3D convolutions whereas most prostate MR volumes have anisotropic spatial resolution. Besides, the fully convolutional structural methods achieve good performance for localization issues but neglect the per-voxel classification for segmentation tasks. In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation. We first design a 3D ResNet encoder to extract 3D features from prostate scans, and then develop the decoder, which is composed of a multi-scale 3D global convolutional block and a 3D boundary refinement block, to address the classification and localization issues simultaneously for volumetric segmentation. Additionally, we combine the encoder-decoder segmentation network with an adversarial network in the training phrase to enforce the contiguity of long-range spatial predictions. Throughout the proposed model, we use anisotropic convolutional processing for better feature learning on prostate MR scans. We evaluated our 3D GCA-Net model on two public prostate MR datasets and achieved state-of-the-art performances.
A Learning-Based Coexistence Mechanism for LAA-LTE Based HetNets
Venue: arXiv cs.NI 2018
Year: 2018

License-assisted access LTE (LAA-LTE) has been proposed to deal with the intense contradiction between tremendous mobile traffic demands and crowded licensed spectrums. In this paper, we investigate the coexistence mechanism for LAA-LTE based heterogenous networks (HetNets). A joint resource allocation and network access problem is considered to maximize the normalized throughput of the unlicensed band while guaranteeing the quality-of-service requirements of incumbent WiFi users. A two-level learning-based framework is proposed to solve the problem by decomposing it into two subproblems. In the master level, a Q-learning based method is developed for the LAA-LTE system to determine the proper transmission time. In the slave one, a game-theory based learning method is adopted by each user to autonomously perform network access. Simulation results demonstrate the effectiveness of the proposed solution.
On Evaluation of Embodied Navigation Agents
Venue: arXiv cs.AI 2018
Year: 2018

Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.
Resilient Feedback Controller Design For Linear Model of Power Grids
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, a resilient controller is designed for the linear time-invariant (LTI) systems subject to attacks on the sensors and the actuators. A novel probabilistic attack model is proposed to capture vulnerabilities of the communication links from sensors to the controller and from the controller to actuators. The observer and the controller formulation under the attack are derived. Thereafter, By leveraging Lyapunov functional methods, it is shown that exponential mean-square stability of the system under the output feedback controller is guaranteed if a certain LMI is feasible. The simulation results show the effectiveness and applicability of the proposed controller design approach.
An Attention-Based Approach for Single Image Super Resolution
Venue: arXiv cs.CV 2018
Year: 2018

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.
Delay Minimization for NOMA-MEC Offloading
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers the minimization of the offloading delay for non-orthogonal multiple access assisted mobile edge computing (NOMA-MEC). By transforming the delay minimization problem into a form of fractional programming, two iterative algorithms based on Dinkelbach's method and Newton's method are proposed. The optimality of both methods is proved and their convergence is compared. Furthermore, criteria for choosing between three possible modes, namely orthogonal multiple access (OMA), pure NOMA, and hybrid NOMA, for MEC offloading are established.
The Scheduler is Very Powerful in Competitive Analysis of Distributed List Accessing
Venue: arXiv cs.DS 2018
Year: 2018

This work is a continuation of efforts to define and understand competitive analysis of algorithms in a distributed shared memory setting, which is surprisingly different from the classical online setting. In fact, in a distributed shared memory setting, we find a counter-example to the theorem concerning classical randomized online algorithms which shows that, if there is a $c$-competitive randomized algorithm against an adaptive offline adversary, then there is a $c$-competitive deterministic algorithm [Ben-David, Borodin, Karp, Tardos, Wigderson, 1994]. In a distributed setting, there is additional lack of knowledge concerning what the other processes have done. There is also additional power for the adversary, having control of the scheduler which decides when each process is allowed to take steps. We consider the list accessing problem, which is a benchmark problem for sequential online algorithms. In the distributed version of this problem, each process has its own finite sequence of requests to a shared list. The scheduler arises as a major issue in its competitive analysis. We introduce two different adversaries, which differ in how they are allowed to schedule processes, and use them to perform competitive analysis of distributed list accessing. We prove tight upper and lower bounds on combinatorial properties of merges of the request sequences, which we use in the analysis. Our analysis shows that the effects of the adversarial scheduler can be quite significant, dominating the usual quality loss due to lack of information about the future.
Computed Tomography Image Enhancement using 3D Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Computed tomography (CT) is increasingly being used for cancer screening, such as early detection of lung cancer. However, CT studies have varying pixel spacing due to differences in acquisition parameters. Thick slice CTs have lower resolution, hindering tasks such as nodule characterization during computer-aided detection due to partial volume effect. In this study, we propose a novel 3D enhancement convolutional neural network (3DECNN) to improve the spatial resolution of CT studies that were acquired using lower resolution/slice thicknesses to higher resolutions. Using a subset of the LIDC dataset consisting of 20,672 CT slices from 100 scans, we simulated lower resolution/thick section scans then attempted to reconstruct the original images using our 3DECNN network. A significant improvement in PSNR (29.3087dB vs. 28.8769dB, p-value < 2.2e-16) and SSIM (0.8529dB vs. 0.8449dB, p-value < 2.2e-16) compared to other state-of-art deep learning methods is observed.
Smoothed Analysis of the Expected Number of Maximal Points in Two Dimensions
Venue: arXiv cs.CG 2018
Year: 2018

The {\em Maximal} points in a set S are those that aren't {\em dominated} by any other point in S. Such points arise in multiple application settings in which they are called by a variety of different names, e.g., maxima, Pareto optimums, skylines. Because of their ubiquity, there is a large literature on the {\em expected} number of maxima in a set S of n points chosen IID from some distribution. Most such results assume that the underlying distribution is uniform over some spatial region and strongly use this uniformity in their analysis. This work was initially motivated by the question of how this expected number changes if the input distribution is perturbed by random noise. More specifically, let Ballp denote the uniform distribution from the 2-d unit Lp ball, delta Ballq denote the 2-d Lq-ball, of radius delta and Ballpq be the convolution of the two distributions, i.e., a point v in Ballp is reported with an error chosen from delta Ballq. The question is how the expected number of maxima change as a function of delta. Although the original motivation is for small delta the problem is well defined for any delta and our analysis treats the general case. More specifically, we study, as a function of n,δ, the expected number of maximal points when the n points in S are chosen IID from distributions of the type Ballpq where p,q in {1,2,infty} for delta > 0 and also of the type Ballp infty-q, where q in [1,infty) for delta > 0.
Moving Beyond the Mean: Analyzing Variance in Software Engineering Experiments
Venue: arXiv cs.SE 2018
Year: 2018

Software Engineering (SE) experiments are traditionally analyzed with statistical tests (e.g., t-tests, ANOVAs, etc.) that assume equally spread data across treatments (i.e., the homogeneity of variances assumption). Differences across treatments' variances in SE are not seen as an opportunity to gain insights on technology performance, but instead, as a hindrance to analyze the data. We have studied the role of variance in mature experimental disciplines such as medicine. We illustrate the extent to which variance may inform on technology performance by means of simulation. We analyze a real-life industrial experiment on Test-Driven Development (TDD) where variance may impact technology desirability. Evaluating the performance of technologies just based on means-as traditionally done in SE-may be misleading. Technologies that make developers resemble more to each other (i.e., technologies with smaller variances) may be more suitable if the aim is minimizing the risk of adopting them in real practice.